title: Experimental comparison of classification methods for key kinase identification for neurite elongation.
abstract: Kinases in a developing neuron play important roles in elongating a neurite with their complex interactions. To elucidate the effect of each kinase on neurite elongation and regeneration from a small set of experiments, we applied machine learning methods to synthetic datasets based on a biologically feasible model. The result showed the ridged partial least squares (RPLS) algorithm performed better than other standard algorithms such as naive Bayes classifier, support vector machines and random forest classification. This suggests the effectiveness of dimension reduction done in RPLS.
authors.forename: Yuji
authors.initials: Y
authors.lastname: Yoshida
authors.forename: Kei
authors.initials: K
authors.lastname: Majima
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Yamada
authors.forename: Yuki
authors.initials: Y
authors.lastname: Maruno
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Sakumura
authors.forename: Kazushi
authors.initials: K
authors.lastname: Ikeda
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D006801:Humans; D016018:Least-Squares Analysis; D058977:Molecular Sequence Annotation; D016501:Neurites / Q000201:enzymology*; D010770:Phosphotransferases / Q000145:classification*; D060388:Support Vector Machine
chemical_list: D010770:Phosphotransferases